Deep Leaf Segmentation Using Synthetic Data

Understand

Automated segmentation of individual leaves of a plant in an image is a prerequisite to measure more complex phenotypic traits in high-throughput phenotyping.

• Applying state-of-the-art machine learning approaches to tackle leaf instance segmentation requires a large amount of manually annotated training data.
• Currently, the benchmark datasets for leaf segmentation contain only a few hundred labeled training images.
• In this paper, we propose a framework for leaf instance segmentation by augmenting real plant datasets with generated synthetic images of plants inspired by domain randomisation.